Machine learning based image processing techniques

ABSTRACT

A machine learning based image processing architecture and associated applications are disclosed herein. In some embodiments, a machine learning framework is trained to learn low level image attributes such as object/scene types, geometries, placements, materials and textures, camera characteristics, lighting characteristics, contrast, noise statistics, etc. Thereafter, the machine learning framework may be employed to detect such attributes in other images and process the images at the attribute level.

CROSS REFERENCE TO OTHER APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/541,603 entitled DIRECT AND DERIVED 3D DATA FOR MACHINE LEARNINGIN IMAGE BASED APPLICATIONS filed Aug. 4, 2017, which is incorporatedherein by reference for all purposes.

BACKGROUND OF THE INVENTION

Image processing techniques have classically operated at a pixel level,i.e., on pixel values or intensities. However, operating on low-levelpixel data is not practical for applications such as altering thehigh-level visual appearance of an image. For such tasks, feature-basedapproaches are more effective. Feature-based techniques include firstdefining a set of specific features (e.g., edges, patches, SURF, SIFT,etc.) and then defining mathematical models on those features that canbe used to analyze and manipulate image content.

Machine learning techniques may be employed to learn either or bothfeatures and mathematical model parameters based on pertinentapplication dependent cost functions. However, such artificialintelligence techniques require an exhaustive training data set thatspans the space of all features for a particular application and that islabeled with relevant ground truth data. It has typically beenprohibitive to gather exhaustive training data sets for most usefulapplications. Furthermore, it has been difficult to label images withmore complex or sophisticated ground truth data. Thus, the use ofmachine learning techniques until lately has been limited to basicobject recognition or classification applications.

An image processing architecture that overcomes such limitations andeffectively leverages machine learning techniques is thus needed anddisclosed herein as well as novel image-based applications resultingtherefrom.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a high level block diagram of an embodiment of a machinelearning based image processing framework for learning attributesassociated with datasets.

FIG. 2 illustrates examples of curated images from a catalog.

FIG. 3 is a high level block diagram of an embodiment of an imageprocessing framework for reducing or decomposing images belonging to aset into key attributes.

FIG. 4 is a high level block diagram of an embodiment of an imageprocessing framework for automatically generating curated images.

FIG. 5 is a high level block diagram of an embodiment of a machinelearning based image processing architecture.

FIG. 6A is a high level block diagram of an embodiment of an imageprocessing application for restyling.

FIG. 6B illustrates an example of using a restyling application.

FIG. 7A is a high level block diagram of an embodiment of an imageprocessing application for replacing an object in an image with adifferent object.

FIG. 7B illustrates an example of using an object replacementapplication.

FIG. 8A is a high level block diagram of an embodiment of an imageprocessing application for denoising an image.

FIG. 8B illustrates an example of using a denoising application.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims,and the invention encompasses numerous alternatives, modifications, andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example, andthe invention may be practiced according to the claims without some orall of these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

An image processing architecture and image-based applications resultingtherefrom are disclosed herein. Generally, images may comprise scenesincluding a single object, a plurality of objects, or rich (virtual)environments and furthermore may comprise stills or frames of animationor video sequences. Moreover, images may comprise (high quality)photographs or (photorealistic) renderings.

Artificial intelligence techniques including machine learning techniquesare data driven and therefore require exhaustive training data sets tobe effective for most practical applications. Thus, the basis for usingmachine learning techniques for image-based applications relies onaccess to comprehensive training data sets that have been labeled ortagged with appropriate metadata that is relevant for desiredapplications.

In this description, machine learning techniques are generally describedand in various embodiments may comprise the use of any combination ofone or more machine learning architectures appropriate for a givenapplication, such as deep neural networks and convolutional neuralnetworks. Moreover, the terms “labels”, “tags”, and “metadata” areinterchangeably used in this description to refer to properties orattributes associated and persisted with a unit of data or content, suchas an image.

A fundamental aspect of the disclosed image processing architecturecomprises a content generation platform for generating comprehensiveimage data sets labeled with relevant ground truth data. Such data setsmay be populated, for example, with images rendered fromthree-dimensional (polygon mesh) models and/or captured from imagingdevices such as cameras or scanning devices such as 3D scanners. Imagedata sets encompassing exhaustive permutations of individual and/orcombinations of objects and arrangements, camera perspectives or poses,lighting types and locations, materials and textures, etc., may begenerated, for example, during offline processes. Image assets maymoreover be obtained from external sources. Metadata may be generatedand associated with (e.g., used to label or tag) images when the imagesare generated at the time of generation and/or may be added or modifiedafterwards and furthermore may be automatically generated and/or may atleast in part be determined or defined manually.

FIG. 1 is a high level block diagram of an embodiment of a machinelearning based image processing framework 100 for learning attributesassociated with datasets. Image datasets 104 are collected or generated.In many cases, an image dataset 104 comprises high quality (i.e., highdefinition or resolution) photographs or photorealistic renderings. Animage dataset 104 may at least in part be populated by images renderedfrom three-dimensional models 102 as depicted in the example of FIG. 1,images captured from imaging or scanning devices, images sourced fromexternal entities, or images generated from other existing images usingvarious processing techniques. Generally, datasets 104 includes anyarbitrary number of permutations of different perspectives orviewpoints, materials and textures, lighting sources and locations,camera configurations, object combinations and placements, etc.

Images comprising datasets 104 are tagged with comprehensive sets oflabels or metadata. A set of labels defined and/or selected for imagesof a prescribed dataset may at least in part be application dependent.The set of labels may include one or more hierarchical high level labelsthat provide classification of the object(s) and/or scene comprising animage. The set of labels may furthermore include lower level labelscomprising ground truth data associated with rendering an image or partsthereof from underlying three-dimensional model(s) or capturing theimage using a physical device such as a camera or a scanner. Examples ofsuch labels include labels associated with the (three-dimensional)geometry of the scene comprising the image such as object types andlocations, material properties of objects comprising the scene, surfacenormal vectors, lighting types and positions (e.g., direct sources aswell as indirect sources such as reflective surfaces that contribute tosubstantial higher order bounces), camera characteristics (e.g.,perspective or pose, orientation, rotation, depth information, focallength, aperture, zoom level), etc. The labels may include absoluteand/or relative location or position, orientation, and depth informationamongst various scene objects, light sources, and the (virtual) camerathat captures the scene. Other labels that are not scene-based butinstead image-based such as noise statistics (e.g., the number ofsamples of rays used in ray tracing when rendering) may be associatedwith an image. Furthermore, more complex or sophisticated labels may bedefined for various applications by combining a plurality of otherlabels. Examples include defining labels for specific object posesrelative to light sources, presence of special materials (e.g., leather)with specific light sources, etc.

As described, knowledge of ground truth data associated with generatingimages comprising a dataset 104 facilitates detailed as well as custom(e.g., application specific) labels to be associated with the imagescomprising the dataset 104, including many types and classes of labelsthat otherwise could not be manually identified and associated withimages such as lighting type and location. Extensive, labeled datasets104 are perfectly qualified for artificial intelligence based learning.Training 106 on a dataset 104, for example, using any combination of oneor more appropriate machine learning techniques such as deep neuralnetworks and convolutional neural networks, results in a set of one ormore low level properties or attributes 110 associated with the dataset104 to be learned. Such attributes may be derived or inferred fromlabels of the dataset 104. Examples of attributes that may be learnedinclude attributes associated with object/scene types and geometries,materials and textures, camera characteristics, lightingcharacteristics, noise statistics, contrast (e.g., global and/or localimage contrast defined by prescribed metrics, which may be based on, forinstance, maximum and minimum pixel intensity values), etc. Other moreintangible attributes that are (e.g., some unknown nonlinear)combinations or functions of a plurality of low level attributes mayalso be learned. Examples of such intangible attributes includeattributes associated with style, aesthetic, noise signatures, etc. Invarious embodiments, different training models may be used to learndifferent attributes. Furthermore, image processing framework 100 may betrained with respect to a plurality of different training datasets.After training on large sets of data to learn various attributes, imageprocessing framework 100 may subsequently be employed to detect similarattributes or combinations thereof in other images for which suchattributes are unknown as further described below.

Many applications include the creation of collections or portfolios ofcurated images that share one or more attributes or properties that aredifficult to quantify or define but that collectively contribute to adistinct signature style or visual appearance. For example, imagescomprising product catalogs published by high end merchants or retailerstypically have a specific branded style or aesthetic, and similarlyframes of scenes of animation or video sequences are all oftenconstrained to prescribed visual characteristics. In such applications,captured photographs or generated renderings are often subjected tomanual post-processing (e.g., retouching and remastering) by artists tocreate publishable imagery having visual characteristics that conform toa desired or sanctioned style or aesthetic or theme. Many of theproperties that contribute to style or aesthetic or theme are the resultof artist manipulation beyond anything that can be achieved fromphotography/rendering or global post-processing. Thus, an artistimparted aesthetic on an image is an intangible quality that hasconventionally been difficult to isolate, model, and replicate. As aresult, many existing applications still require artists to manuallypost-process each image or frame to obtain a desired curated look.

FIG. 2 illustrates examples of curated images from a catalog of a homefurnishings retailer. As depicted, each image comprises differentproducts as well as product placements, but the entire set of imagesshares the same high level style or aesthetic defining an inspired,branded look. The curated images of FIG. 2 result from post-processingby an artist and are not specifically labeled with low level attributesor ground truth data. However, as can be seen, all images belong to aset having common characteristics other than just having similar typesof content (i.e., home furnishings). In the given example, all imagesshare the same aesthetic particular to a prescribed brand and thusvisually appear similar and are recognizable as belonging to the sameset. In some embodiments, the high level attribute of the set of imagesthat results in the shared similar look may be modeled by a nonlinearfunction of one or more lower level attributes that collectively definean aesthetic as further described next.

FIG. 3 is a high level block diagram of an embodiment of an imageprocessing framework 300 for reducing or decomposing images belonging toa set into key attributes, one or more of which may collectively definea high level aesthetic attribute of the set. Framework 300 operates onimages 302 belonging to a set. Images 302 may comprise photographs orrenderings. In some embodiments, the set of images 302 comprises acurated set or catalog of images (e.g., such as the images of FIG. 2)that results from artist post-processing or manipulation to achieve aprescribed style or aesthetic or theme. In some cases, the images 302are not labeled or tagged, e.g., with ground truth data. However, images302 belong to the same set and, thus, share one or more commonproperties or attributes such as content type as well as visualcharacteristics and appearance. The set of images 302 are processed by amachine learning based framework 304 (e.g., which may comprise framework100 of FIG. 1) to detect or identify a set of one or more high and lowlevel attributes 306 associated with the set of images 302.

In some cases, machine learning based framework 304 is trained on largelabeled image datasets comprising a substantial subset of, if not all,possible permutations of objects of a constrained set of possibleobjects that may appear in a prescribed scene type in order to learnassociated attributes and combinations thereof and which maysubsequently be employed to detect or identify such attributes in otherimages such as a corresponding set of curated or artist processed images302 that include objects from the same constrained set of possibleobjects. Examples of attributes 306 that may be detected for the set ofimages 302 include object/scene types and geometries, materials andtextures, camera characteristics, lighting characteristics, noisestatistics, contrast, etc. In some embodiments, a high level aestheticattribute that results in images 302 having a shared look is defined bya (e.g., unknown nonlinear) function or combination of a plurality ofidentified low level attributes 306. The described machine learningbased framework 304 facilitates identifying shared properties of imagesbelonging to a set 302 and identifying and isolating a high level sharedstyle or aesthetic attribute that includes artist impartedcharacteristics. Attributes 306 identified for the set of images 302 maybe employed to automatically label or tag images 302 that do not alreadyhave such labels or tags.

As previously described, many applications require artists topost-process images to impart prescribed styles or aesthetics. Thus, insuch applications, curated imagery suitable for publication is oftenlimited to a small number of viable shots that conform to a desiredstyle or aesthetic. It would be useful to automatically generate moreextensive sets of curated images, for example, that have prescribedstyles or aesthetics but without requiring artists to impart the stylesor aesthetics via post-processing. FIG. 4 is a high level block diagramof an embodiment of an image processing framework 400 for automaticallygenerating curated images without artist input. As depicted, a set ofattributes 402, e.g., identified from a small set of artist createdcurated images such as using framework 300 of FIG. 3, is applied toavailable three-dimensional object models 404 with configurableproperties (geometries, lightings, materials, camera poses, etc.) torender any number of images 406 having attributes 402. Rendered images406 automatically have the curated visual appearance that is defined byattributes 402 but without requiring artist input or post-processing.

In some embodiments, three-dimensional (polygon mesh) models for most,if not all, individual objects comprising a constrained set of possibleobjects that may be included in a prescribed scene type exist and may beemployed to automatically render any number of additional curated shotsof the scene type without artist input but having the same aesthetic orstyle as a relatively small set of artist created base imagery fromwhich aesthetic or style attributes are identified, for example, usingframework 300 of FIG. 3. In some cases, a super catalog of curatedimages for a prescribed scene type may be generated of which only asmall subset of shots are artist created and the rest are renderedautomatically using framework 400. In some cases, a super catalogincludes images comprising many, if not all, possible permutations andcombinations of objects, materials, lightings, camera poses, placements,etc., associated with a particular scene space that all exhibit aprescribed style or aesthetic defined by attributes 402 but withoutrequiring artist post-processing to impart the style or aesthetic. Thatis, aesthetic attributes isolated using framework 300 of FIG. 3 may beused to render additional new images having that same aesthetic (i.e.,the same low level attributes 306/402 that define the aesthetic) usingthe three-dimensional object models of framework 400. Images 406generated using framework 400 may automatically be labeled or taggedwith appropriate metadata or ground truth data since such images arerendered from well-defined three-dimensional models and knownattributes. Moreover, large sets of labeled images 406 that aregenerated may be employed to further train and build an associatedmachine learning based framework (e.g., framework 100 of FIG. 1).

FIG. 5 is a high level block diagram of an embodiment of a machinelearning based image processing architecture 500. As depicted, imageprocessing architecture 500 includes many components that haveseparately been described in detail with respect to FIGS. 1-4. Machinelearning framework 501 (which may comprise framework 100 of FIG. 1) isthe foundation of image processing architecture 500 and trains on largedatasets, e.g., which may at least in part be generated from availablethree-dimensional models, to learn attributes associated with thedatasets. Machine learning framework 501 may then be used, for example,with other images to detect or identify such attributes or combinationsthereof, which may not be known for the images prior to detection bymachine learning framework 501.

In one example, machine learning framework 501 identifies attributesthat collectively define an aesthetic 504 of a small set of curatedcatalog images 502 that have been post-processed by artists to have theaesthetic. In some cases, the isolated aesthetic (i.e., correspondingattributes) 504 may be applied to available three-dimensional models togenerate a super catalog 506 of additional curated catalog images thatall have that aesthetic but without requiring artist post-processinglike the original set 502. Super catalog 506 may be used to furthertrain and build machine learning framework 501. Thus, aesthetics orstyles or themes may be identified using machine learning framework 501and then applied to available three-dimensional models to generateadditional datasets on which machine learning framework 501 may befurther trained.

Machine learning framework 501 generally facilitates a variety of imageprocessing applications 508 for modifying input images 510 or partsthereof to generate corresponding output images 512 having the desiredmodifications. Both high and low level attributes associated with imagesare detectable, and high level attributes are separable into constituentlower level attributes. Thus, independent decisions can be made ondifferent attributes or combinations of attributes in variousapplications. Some example image processing applications 508 includerestyling (e.g., changing aesthetic), object replacement, relighting(e.g., changing light source types and/or locations), etc., a few ofwhich are further described next.

FIG. 6A is a high level block diagram of an embodiment of an imageprocessing application 600 for restyling, and FIG. 6B illustrates anexample of using restyling application 600. As depicted, machinelearning framework 601 (e.g., framework 100 of FIG. 1 or framework 501of FIG. 5) is employed to identify and isolate a catalog aesthetic 604of an image catalog 602 and an image aesthetic 608 of an input image 606so that the image aesthetic 608 can be removed or subtracted from image606 and the catalog aesthetic 604 applied or added to image 606 togenerate an output catalog version 610 of input image 606 that has thesame aesthetic 604 as catalog 602. In this example, the types ofattributes that comprise the catalog aesthetic 604 may be detected andisolated in input image 606 to determine image aesthetic 608. Restylingis illustrated in FIG. 6B in which an input image 606 is restyled byrestyling application 600 to generate a catalog image 610 that conformsto a prescribed catalog aesthetic 604. In this example, changing theaesthetic comprises modifying the lighting or brightness at differentparts of the image in different manners.

FIG. 7A is a high level block diagram of an embodiment of an imageprocessing application 700 for replacing an object in an image with adifferent object, and FIG. 7B illustrates an example of using objectreplacement application 700. In FIG. 7A, a machine learning framework(e.g., framework 100 of FIG. 1 or framework 501 of FIG. 5) is employedto identify attributes 704-708 of an input image 702 that are associatedwith a prescribed object so that the object can be replaced with anotherobject 710. New object 710 may be configured to have some of the sameidentified attributes 704-708 so that it can be consistently included inimage 710 in place of the replaced object. Object replacement isillustrated in FIG. 7B in which a sofa object in input image 702 isreplaced with a different sofa object in output image 710.

Image processing applications 508 may furthermore comprise more complexenterprise applications such as aggregating objects from datasets havingdifferent aesthetic attributes into the same scene and styling to have aprescribed aesthetic. For example, home furnishings objects from one ormore brands may be included in an image of a room but may be all styledto have the aesthetic of a prescribed brand. In such cases, theresulting image would have the curated look or aesthetic of a catalogimage of the prescribed brand.

Generally, image processing applications 508 rely on attribute detectionusing machine learning framework 501. That is, actual attributes used togenerate images 510 are detected using machine learning framework 501and modified to generate output images 512 having modified attributes.Thus, image modification based on attribute detection, manipulation,and/or modification as described herein is notably different fromconventional image editing applications that operate at pixel levels onpixel values and do not have information on actual image content (e.g.,objects) and underlying attributes associated with the physics ofcapturing or rendering the content such as geometry, camera, lighting,materials, textures, placements, etc., on which the disclosed techniquesare based. The disclosed attribute detection and manipulation techniquesare especially useful for photorealistic applications becauseconventional pixel manipulations are not sufficiently constrained togenerate images that look real and consistent.

Another useful application 508 based on machine learning framework 501comprises image denoising. One or more learned spatial filters may beapplied to various parts of a noisy input image 510 (e.g., that isgenerated using a low number of samples of rays during ray tracing) toremove noise so that output image 512 has a noise profile or qualitythat is comparable to that achievable using a large number of samples ofrays. That is, various parts of a sparsely sampled image are filteredusing a set of filters identified by machine learning framework 501 togenerate an output image equivalent to ray tracing with a much largernumber of samples (e.g., a number of samples needed for completeconvergence). As one example, a ten sample ray traced image can quicklybe transformed into the equivalent of a corresponding thousand sampleray traced image by filtering the ten sample ray traced image withappropriate filters identified by machine learning framework 501. Thus,image render time can substantially be reduced by only ray tracing witha small number of samples and then using filters that predict pixelvalues that would result from larger numbers of samples. This techniqueeffectively eliminates the need to ray trace with large numbers ofsamples while still producing images having qualities or noise profilesthat ray tracing with large numbers of samples provides.

In some embodiments, training datasets for such a denoising applicationcomprise ray traced snapshots of images at different sampling intervals,with each snapshot labeled with an attribute specifying the number ofsamples for that snapshot in addition to being labeled with other imageattributes. Machine learning framework 501 trains on such datasets tolearn spatial filters or parameters thereof for different numbers ofsamples. For example, filters may be learned for transforming from a lownumber (x) of samples to a high number (y) of samples for many differentvalues and combinations of x and y, where x<<y. Noise signatures,however, are not only based on numbers of samples but also on one ormore other image attributes that affect noise (e.g., during ray tracing)such as materials and lighting. Thus, different filter parameters may belearned for attribute combinations that result in different noisesignatures, and a machine learning framework 501 that identifies filtersfor an input image may identify different filters or parameters fordifferent portions of the image. For example, a different set of filterparameters may be identified for a portion of an image that hasattribute combination “ten samples on leather with bright light” than aportion of the image that has attribute combination “ten samples onfabric in dim light”.

FIG. 8A is a high level block diagram of an embodiment of an imageprocessing application 800 for denoising an image, and FIG. 8Billustrates an example of using denoising application 800. In FIG. 8A, anoisy (e.g., low sample count) input image 802 that violates aprescribed noise threshold is processed using one or more filters 804that are identified by a machine learning based framework (e.g.,framework 100 of FIG. 1 or framework 501 of FIG. 5) to generate adenoised output image 806 that satisfies the prescribed noise threshold.Thus, a noisy image 802 rendered using a low number of samples of raysis cleaned up or denoised 806 using learned filters 804, eliminating theneed to actually ray trace with a large number of samples. Denoising isillustrated in FIG. 8B in which a sparsely sampled input image 802 isprocessed by application 800 to generate a high quality output image806.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A method, comprising: using a machine learningframework to identify a set of filters for removing noise from asparsely ray traced input image, wherein different filters areidentified for different portions of the input image and wherein the setof filters predicts pixel values that would result from ray tracing withmore samples of rays; and outputting an output image comprising afiltered version of the input image resulting from filtering the inputimage with the identified set of filters, wherein a quality of theoutput image is equivalent to ray tracing with more samples of rays thanthe sparsely ray traced input image.
 2. The method of claim 1, whereinthe output image is a denoised version of the input image.
 3. The methodof claim 1, wherein the sparsely ray traced input image violates a noisethreshold and the output image satisfies the noise threshold.
 4. Themethod of claim 1, wherein the sparsely ray traced input image is raytraced with a small number of samples of rays.
 5. The method of claim 1,wherein the quality of the output image is equivalent to ray tracingwith a large number of samples of rays.
 6. The method of claim 1,wherein the quality of the output image is equivalent to ray tracingwith a number of samples of rays needed for complete convergence.
 7. Themethod of claim 1, wherein the method facilitates substantially reducingimage render time.
 8. The method of claim 1, wherein the filterscomprise spatial filters.
 9. The method of claim 1, wherein to identifya set of filters comprises to identify a set of one or more filterparameters.
 10. The method of claim 1, wherein the method facilitateseffectively eliminating ray tracing with large numbers of samples. 11.The method of claim 1, wherein a prescribed filter of the set of filtersis associated with a corresponding noise signature.
 12. The method ofclaim 11, wherein the noise signature is based on one or moreattributes.
 13. The method of claim 12, wherein the attributes compriseone or more attributes associated with object/scene types, geometries,placements, materials, textures, camera characteristics, lightingcharacteristics, numbers of samples, and contrast.
 14. The method ofclaim 1, wherein the machine learning framework is trained to learnnoise signatures and corresponding filters.
 15. The method of claim 1,wherein the machine learning framework is trained on image datasetscomprising ray traced snapshots at different sampling intervals.
 16. Themethod of claim 1, wherein the machine learning framework is trained onimage datasets comprising permutations of a constrained set of objectsassociated with a prescribed scene type to which the input imagebelongs.
 17. The method of claim 1, wherein the output image comprises aphotorealistic rendering.
 18. The method of claim 1, wherein the outputimage comprises a frame of an animation or a video sequence.
 19. Asystem, comprising: a processor configured to: use a machine learningframework to identify a set of filters for removing noise from asparsely ray traced input image, wherein different filters areidentified for different portions of the input image and wherein the setof filters predicts pixel values that would result from ray tracing withmore samples of rays; and output an output image comprising a filteredversion of the input image resulting from filtering the input image withthe identified set of filters, wherein a quality of the output image isequivalent to ray tracing with more samples of rays than the sparselyray traced input image; and a memory coupled to the processor andconfigured to provide the processor with instructions.
 20. A computerprogram product embodied in a non-transitory computer readable storagemedium and comprising computer instructions for: using a machinelearning framework to identify a set of filters for removing noise froma sparsely ray traced input image, wherein different filters areidentified for different portions of the input image and wherein the setof filters predicts pixel values that would result from ray tracing withmore samples of rays; and outputting an output image comprising afiltered version of the input image resulting from filtering the inputimage with the identified set of filters, wherein a quality of theoutput image is equivalent to ray tracing with more samples of rays thanthe sparsely ray traced input image.
 21. The system of claim 19, whereinthe output image is a denoised version of the input image.
 22. Thesystem of claim 19, wherein the sparsely ray traced input image violatesa noise threshold and the output image satisfies the noise threshold.23. The system of claim 19, wherein the sparsely ray traced input imageis ray traced with a small number of samples of rays.
 24. The system ofclaim 19, wherein the quality of the output image is equivalent to raytracing with a large number of samples of rays.
 25. The system of claim19, wherein the quality of the output image is equivalent to ray tracingwith a number of samples of rays needed for complete convergence. 26.The system of claim 19, wherein the system facilitates substantiallyreducing image render time.
 27. The system of claim 19, wherein thefilters comprise spatial filters.
 28. The system of claim 19, wherein toidentify a set of filters comprises to identify a set of one or morefilter parameters.
 29. The system of claim 19, wherein the systemfacilitates effectively eliminating ray tracing with large numbers ofsamples.
 30. The system of claim 19, wherein a prescribed filter of theset of filters is associated with a corresponding noise signature. 31.The system of claim 30, wherein the noise signature is based on one ormore attributes.
 32. The system of claim 31, wherein the attributescomprise one or more attributes associated with object/scene types,geometries, placements, materials, textures, camera characteristics,lighting characteristics, numbers of samples, and contrast.
 33. Thesystem of claim 19, wherein the machine learning framework is trained tolearn noise signatures and corresponding filters.
 34. The system ofclaim 19, wherein the machine learning framework is trained on imagedatasets comprising ray traced snapshots at different samplingintervals.
 35. The system of claim 19, wherein the machine learningframework is trained on image datasets comprising permutations of aconstrained set of objects associated with a prescribed scene type towhich the input image belongs.
 36. The system of claim 19, wherein theoutput image comprises a photorealistic rendering.
 37. The system ofclaim 19, wherein the output image comprises a frame of an animation ora video sequence.
 38. The computer program product of claim 20, whereinthe output image is a denoised version of the input image.
 39. Thecomputer program product of claim 20, wherein the sparsely ray tracedinput image violates a noise threshold and the output image satisfiesthe noise threshold.
 40. The computer program product of claim 20,wherein the sparsely ray traced input image is ray traced with a smallnumber of samples of rays.
 41. The computer program product of claim 20,wherein the quality of the output image is equivalent to ray tracingwith a large number of samples of rays.
 42. The computer program productof claim 20, wherein the quality of the output image is equivalent toray tracing with a number of samples of rays needed for completeconvergence.
 43. The computer program product of claim 20, wherein thecomputer program product facilitates substantially reducing image rendertime.
 44. The computer program product of claim 20, wherein the filterscomprise spatial filters.
 45. The computer program product of claim 20,wherein to identify a set of filters comprises to identify a set of oneor more filter parameters.
 46. The computer program product of claim 20,wherein the computer program product facilitates effectively eliminatingray tracing with large numbers of samples.
 47. The computer programproduct of claim 20, wherein a prescribed filter of the set of filtersis associated with a corresponding noise signature.
 48. The computerprogram product of claim 47, wherein the noise signature is based on oneor more attributes.
 49. The computer program product of claim 48,wherein the attributes comprise one or more attributes associated withobject/scene types, geometries, placements, materials, textures, cameracharacteristics, lighting characteristics, numbers of samples, andcontrast.
 50. The computer program product of claim 20, wherein themachine learning framework is trained to learn noise signatures andcorresponding filters.
 51. The computer program product of claim 20,wherein the machine learning framework is trained on image datasetscomprising ray traced snapshots at different sampling intervals.
 52. Thecomputer program product of claim 20, wherein the machine learningframework is trained on image datasets comprising permutations of aconstrained set of objects associated with a prescribed scene type towhich the input image belongs.
 53. The computer program product of claim20, wherein the output image comprises a photorealistic rendering. 54.The computer program product of claim 20, wherein the output imagecomprises a frame of an animation or a video sequence.